Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2211
Missing cells763
Missing cells (%)1.7%
Duplicate rows12
Duplicate rows (%)0.5%
Total size in memory293.8 KiB
Average record size in memory136.1 B

Variable types

DateTime1
Categorical4
Numeric10
Text5

Alerts

indicativo has constant value "6076X" Constant
nombre has constant value "MARBELLA, PUERTO" Constant
provincia has constant value "MALAGA" Constant
altitud has constant value "2.0" Constant
Dataset has 12 (0.5%) duplicate rowsDuplicates
hrMax is highly overall correlated with hrMedia and 1 other fieldsHigh correlation
hrMedia is highly overall correlated with hrMax and 1 other fieldsHigh correlation
hrMin is highly overall correlated with hrMax and 1 other fieldsHigh correlation
racha is highly overall correlated with velmediaHigh correlation
tmax is highly overall correlated with tmed and 1 other fieldsHigh correlation
tmed is highly overall correlated with tmax and 1 other fieldsHigh correlation
tmin is highly overall correlated with tmax and 1 other fieldsHigh correlation
velmedia is highly overall correlated with rachaHigh correlation
tmed has 25 (1.1%) missing values Missing
prec has 416 (18.8%) missing values Missing
tmin has 25 (1.1%) missing values Missing
horatmin has 25 (1.1%) missing values Missing
tmax has 25 (1.1%) missing values Missing
horatmax has 25 (1.1%) missing values Missing
prec has 1585 (71.7%) zeros Zeros

Reproduction

Analysis started2025-02-25 23:04:51.989290
Analysis finished2025-02-25 23:05:02.033189
Duration10.04 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

fecha
Date

Distinct2199
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2019-01-01 00:00:00
Maximum2025-01-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-25T23:05:02.108406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-25T23:05:02.236813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

indicativo
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.4 KiB
6076X
2198 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10990
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6076X
2nd row6076X
3rd row6076X
4th row6076X
5th row6076X

Common Values

ValueCountFrequency (%)
6076X 2198
99.4%
(Missing) 13
 
0.6%

Length

2025-02-25T23:05:02.328885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T23:05:02.374215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6076x 2198
100.0%

Most occurring characters

ValueCountFrequency (%)
6 4396
40.0%
0 2198
20.0%
7 2198
20.0%
X 2198
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 4396
40.0%
0 2198
20.0%
7 2198
20.0%
X 2198
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 4396
40.0%
0 2198
20.0%
7 2198
20.0%
X 2198
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 4396
40.0%
0 2198
20.0%
7 2198
20.0%
X 2198
20.0%

nombre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.4 KiB
MARBELLA, PUERTO
2198 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters35168
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMARBELLA, PUERTO
2nd rowMARBELLA, PUERTO
3rd rowMARBELLA, PUERTO
4th rowMARBELLA, PUERTO
5th rowMARBELLA, PUERTO

Common Values

ValueCountFrequency (%)
MARBELLA, PUERTO 2198
99.4%
(Missing) 13
 
0.6%

Length

2025-02-25T23:05:02.430448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T23:05:02.472170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
marbella 2198
50.0%
puerto 2198
50.0%

Most occurring characters

ValueCountFrequency (%)
A 4396
12.5%
R 4396
12.5%
L 4396
12.5%
E 4396
12.5%
B 2198
6.2%
M 2198
6.2%
, 2198
6.2%
2198
6.2%
P 2198
6.2%
U 2198
6.2%
Other values (2) 4396
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4396
12.5%
R 4396
12.5%
L 4396
12.5%
E 4396
12.5%
B 2198
6.2%
M 2198
6.2%
, 2198
6.2%
2198
6.2%
P 2198
6.2%
U 2198
6.2%
Other values (2) 4396
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4396
12.5%
R 4396
12.5%
L 4396
12.5%
E 4396
12.5%
B 2198
6.2%
M 2198
6.2%
, 2198
6.2%
2198
6.2%
P 2198
6.2%
U 2198
6.2%
Other values (2) 4396
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4396
12.5%
R 4396
12.5%
L 4396
12.5%
E 4396
12.5%
B 2198
6.2%
M 2198
6.2%
, 2198
6.2%
2198
6.2%
P 2198
6.2%
U 2198
6.2%
Other values (2) 4396
12.5%

provincia
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.4 KiB
MALAGA
2198 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13188
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALAGA
2nd rowMALAGA
3rd rowMALAGA
4th rowMALAGA
5th rowMALAGA

Common Values

ValueCountFrequency (%)
MALAGA 2198
99.4%
(Missing) 13
 
0.6%

Length

2025-02-25T23:05:02.525508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T23:05:02.691052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
malaga 2198
100.0%

Most occurring characters

ValueCountFrequency (%)
A 6594
50.0%
M 2198
 
16.7%
L 2198
 
16.7%
G 2198
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6594
50.0%
M 2198
 
16.7%
L 2198
 
16.7%
G 2198
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6594
50.0%
M 2198
 
16.7%
L 2198
 
16.7%
G 2198
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6594
50.0%
M 2198
 
16.7%
L 2198
 
16.7%
G 2198
 
16.7%

altitud
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.4 KiB
2.0
2198 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6594
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2198
99.4%
(Missing) 13
 
0.6%

Length

2025-02-25T23:05:02.746697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-25T23:05:02.789774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2198
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2198
33.3%
. 2198
33.3%
0 2198
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2198
33.3%
. 2198
33.3%
0 2198
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2198
33.3%
. 2198
33.3%
0 2198
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2198
33.3%
. 2198
33.3%
0 2198
33.3%

tmed
Real number (ℝ)

High correlation  Missing 

Distinct179
Distinct (%)8.2%
Missing25
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean18.902242
Minimum7.8000002
Maximum30.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:02.857188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.8000002
5-th percentile13.025
Q115.5
median18.6
Q322.200001
95-th percentile25.6
Maximum30.6
Range22.799999
Interquartile range (IQR)6.7000008

Descriptive statistics

Standard deviation4.0644083
Coefficient of variation (CV)0.21502256
Kurtosis-0.92839468
Mean18.902242
Median Absolute Deviation (MAD)3.3000002
Skewness0.19237748
Sum41320.3
Variance16.519415
MonotonicityNot monotonic
2025-02-25T23:05:02.958661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.60000038 38
 
1.7%
14.60000038 34
 
1.5%
15.19999981 33
 
1.5%
16.20000076 32
 
1.4%
14.80000019 31
 
1.4%
14.19999981 31
 
1.4%
15.80000019 31
 
1.4%
16.39999962 31
 
1.4%
16.60000038 30
 
1.4%
21.60000038 30
 
1.4%
Other values (169) 1865
84.4%
ValueCountFrequency (%)
7.800000191 1
< 0.1%
9.600000381 1
< 0.1%
9.899999619 1
< 0.1%
10.19999981 1
< 0.1%
10.39999962 2
0.1%
10.60000038 1
< 0.1%
10.80000019 2
0.1%
11 2
0.1%
11.10000038 1
< 0.1%
11.19999981 2
0.1%
ValueCountFrequency (%)
30.60000038 1
 
< 0.1%
30.10000038 1
 
< 0.1%
29.60000038 1
 
< 0.1%
29.20000076 2
 
0.1%
29 1
 
< 0.1%
28.20000076 2
 
0.1%
28 3
0.1%
27.79999924 4
0.2%
27.60000038 1
 
< 0.1%
27.39999962 7
0.3%

prec
Real number (ℝ)

Missing  Zeros 

Distinct72
Distinct (%)4.0%
Missing416
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean0.77036212
Minimum0
Maximum72.599998
Zeros1585
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:03.059005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.4000001
Maximum72.599998
Range72.599998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4019771
Coefficient of variation (CV)5.7141661
Kurtosis105.69124
Mean0.77036212
Median Absolute Deviation (MAD)0
Skewness9.190052
Sum1382.8
Variance19.377401
MonotonicityNot monotonic
2025-02-25T23:05:03.197431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1585
71.7%
0.200000003 36
 
1.6%
0.400000006 23
 
1.0%
0.6000000238 15
 
0.7%
1.399999976 9
 
0.4%
1 8
 
0.4%
2 7
 
0.3%
1.200000048 5
 
0.2%
2.400000095 5
 
0.2%
1.600000024 5
 
0.2%
Other values (62) 97
 
4.4%
(Missing) 416
 
18.8%
ValueCountFrequency (%)
0 1585
71.7%
0.200000003 36
 
1.6%
0.400000006 23
 
1.0%
0.6000000238 15
 
0.7%
0.8000000119 5
 
0.2%
1 8
 
0.4%
1.200000048 5
 
0.2%
1.399999976 9
 
0.4%
1.600000024 5
 
0.2%
1.799999952 3
 
0.1%
ValueCountFrequency (%)
72.59999847 1
< 0.1%
67.19999695 1
< 0.1%
56 1
< 0.1%
43.79999924 1
< 0.1%
37.20000076 1
< 0.1%
35.79999924 1
< 0.1%
34.40000153 1
< 0.1%
33 1
< 0.1%
32.59999847 1
< 0.1%
32 1
< 0.1%

tmin
Real number (ℝ)

High correlation  Missing 

Distinct193
Distinct (%)8.8%
Missing25
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean15.817795
Minimum5.3000002
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:03.311768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.3000002
5-th percentile9.5
Q112.4
median15.6
Q319.1
95-th percentile22.799999
Maximum26.6
Range21.299999
Interquartile range (IQR)6.7000008

Descriptive statistics

Standard deviation4.2075052
Coefficient of variation (CV)0.26599821
Kurtosis-0.8457312
Mean15.817795
Median Absolute Deviation (MAD)3.3000002
Skewness0.11894233
Sum34577.7
Variance17.703102
MonotonicityNot monotonic
2025-02-25T23:05:03.417767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.10000038 28
 
1.3%
14 27
 
1.2%
11.89999962 26
 
1.2%
12.60000038 26
 
1.2%
12.19999981 25
 
1.1%
14.10000038 24
 
1.1%
13.60000038 24
 
1.1%
13 23
 
1.0%
14.39999962 23
 
1.0%
13.10000038 22
 
1.0%
Other values (183) 1938
87.7%
(Missing) 25
 
1.1%
ValueCountFrequency (%)
5.300000191 1
< 0.1%
5.599999905 2
0.1%
6.099999905 1
< 0.1%
6.300000191 2
0.1%
6.400000095 1
< 0.1%
6.5 2
0.1%
6.599999905 1
< 0.1%
6.800000191 1
< 0.1%
6.900000095 1
< 0.1%
7 1
< 0.1%
ValueCountFrequency (%)
26.60000038 2
 
0.1%
26.20000076 1
 
< 0.1%
25.89999962 1
 
< 0.1%
25.5 1
 
< 0.1%
25.29999924 3
0.1%
25.10000038 2
 
0.1%
24.79999924 5
0.2%
24.70000076 5
0.2%
24.60000038 1
 
< 0.1%
24.5 3
0.1%

horatmin
Text

Missing 

Distinct574
Distinct (%)26.3%
Missing25
Missing (%)1.1%
Memory size17.4 KiB
2025-02-25T23:05:03.632999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0494053
Min length5

Characters and Unicode

Total characters11038
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)8.6%

Sample

1st row06:31
2nd row07:32
3rd row06:22
4th row23:35
5th row07:03
ValueCountFrequency (%)
varias 108
 
4.9%
00:01 20
 
0.9%
24:00 18
 
0.8%
23:59 17
 
0.8%
05:18 16
 
0.7%
05:25 14
 
0.6%
06:01 13
 
0.6%
05:37 13
 
0.6%
05:52 13
 
0.6%
05:47 12
 
0.5%
Other values (564) 1942
88.8%
2025-02-25T23:05:03.922582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2429
22.0%
: 2078
18.8%
5 1122
10.2%
2 1008
9.1%
3 908
 
8.2%
4 887
 
8.0%
1 663
 
6.0%
6 528
 
4.8%
7 321
 
2.9%
9 227
 
2.1%
Other values (6) 867
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2429
22.0%
: 2078
18.8%
5 1122
10.2%
2 1008
9.1%
3 908
 
8.2%
4 887
 
8.0%
1 663
 
6.0%
6 528
 
4.8%
7 321
 
2.9%
9 227
 
2.1%
Other values (6) 867
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2429
22.0%
: 2078
18.8%
5 1122
10.2%
2 1008
9.1%
3 908
 
8.2%
4 887
 
8.0%
1 663
 
6.0%
6 528
 
4.8%
7 321
 
2.9%
9 227
 
2.1%
Other values (6) 867
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2429
22.0%
: 2078
18.8%
5 1122
10.2%
2 1008
9.1%
3 908
 
8.2%
4 887
 
8.0%
1 663
 
6.0%
6 528
 
4.8%
7 321
 
2.9%
9 227
 
2.1%
Other values (6) 867
 
7.9%

tmax
Real number (ℝ)

High correlation  Missing 

Distinct202
Distinct (%)9.2%
Missing25
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean21.986871
Minimum9.8999996
Maximum37.599998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:04.019177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.8999996
5-th percentile15.8
Q118.6
median21.700001
Q325.375
95-th percentile28.675001
Maximum37.599998
Range27.699999
Interquartile range (IQR)6.7749991

Descriptive statistics

Standard deviation4.1790648
Coefficient of variation (CV)0.19007092
Kurtosis-0.60116321
Mean21.986871
Median Absolute Deviation (MAD)3.3000011
Skewness0.21813037
Sum48063.3
Variance17.464582
MonotonicityNot monotonic
2025-02-25T23:05:04.115848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.29999924 28
 
1.3%
24.5 27
 
1.2%
17.79999924 27
 
1.2%
25.79999924 26
 
1.2%
20.29999924 25
 
1.1%
17.5 25
 
1.1%
21 24
 
1.1%
24.10000038 24
 
1.1%
17.39999962 24
 
1.1%
18.60000038 22
 
1.0%
Other values (192) 1934
87.5%
(Missing) 25
 
1.1%
ValueCountFrequency (%)
9.899999619 1
< 0.1%
12.39999962 1
< 0.1%
12.5 1
< 0.1%
12.69999981 1
< 0.1%
12.80000019 1
< 0.1%
12.89999962 1
< 0.1%
13.10000038 1
< 0.1%
13.19999981 1
< 0.1%
13.30000019 1
< 0.1%
13.5 1
< 0.1%
ValueCountFrequency (%)
37.59999847 1
< 0.1%
37.5 1
< 0.1%
36.29999924 1
< 0.1%
35.70000076 1
< 0.1%
35.29999924 1
< 0.1%
35.20000076 1
< 0.1%
33.79999924 1
< 0.1%
33.5 1
< 0.1%
33.20000076 1
< 0.1%
33 1
< 0.1%

horatmax
Text

Missing 

Distinct715
Distinct (%)32.7%
Missing25
Missing (%)1.1%
Memory size17.4 KiB
2025-02-25T23:05:04.356424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0128088
Min length5

Characters and Unicode

Total characters10958
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)10.0%

Sample

1st row10:14
2nd row16:04
3rd row13:22
4th rowVarias
5th row10:11
ValueCountFrequency (%)
varias 28
 
1.3%
15:54 12
 
0.5%
15:31 12
 
0.5%
14:48 11
 
0.5%
14:40 11
 
0.5%
14:15 11
 
0.5%
15:55 10
 
0.5%
09:17 10
 
0.5%
15:44 10
 
0.5%
15:37 10
 
0.5%
Other values (705) 2061
94.3%
2025-02-25T23:05:04.673718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2367
21.6%
: 2158
19.7%
0 1251
11.4%
5 946
 
8.6%
4 869
 
7.9%
2 767
 
7.0%
3 714
 
6.5%
7 449
 
4.1%
8 432
 
3.9%
6 424
 
3.9%
Other values (6) 581
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2367
21.6%
: 2158
19.7%
0 1251
11.4%
5 946
 
8.6%
4 869
 
7.9%
2 767
 
7.0%
3 714
 
6.5%
7 449
 
4.1%
8 432
 
3.9%
6 424
 
3.9%
Other values (6) 581
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2367
21.6%
: 2158
19.7%
0 1251
11.4%
5 946
 
8.6%
4 869
 
7.9%
2 767
 
7.0%
3 714
 
6.5%
7 449
 
4.1%
8 432
 
3.9%
6 424
 
3.9%
Other values (6) 581
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2367
21.6%
: 2158
19.7%
0 1251
11.4%
5 946
 
8.6%
4 869
 
7.9%
2 767
 
7.0%
3 714
 
6.5%
7 449
 
4.1%
8 432
 
3.9%
6 424
 
3.9%
Other values (6) 581
 
5.3%

dir
Real number (ℝ)

Distinct37
Distinct (%)1.7%
Missing18
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean24.000912
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2025-02-25T23:05:04.769883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median23
Q327
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.798304
Coefficient of variation (CV)0.90822815
Kurtosis5.9805537
Mean24.000912
Median Absolute Deviation (MAD)11
Skewness2.4206095
Sum52634
Variance475.16606
MonotonicityNot monotonic
2025-02-25T23:05:04.870942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
24 241
 
10.9%
23 182
 
8.2%
34 166
 
7.5%
10 147
 
6.6%
99 138
 
6.2%
8 134
 
6.1%
6 127
 
5.7%
22 122
 
5.5%
12 105
 
4.7%
35 82
 
3.7%
Other values (27) 749
33.9%
ValueCountFrequency (%)
1 22
 
1.0%
2 16
 
0.7%
3 11
 
0.5%
4 29
 
1.3%
5 47
 
2.1%
6 127
5.7%
7 60
2.7%
8 134
6.1%
9 72
3.3%
10 147
6.6%
ValueCountFrequency (%)
99 138
6.2%
36 47
 
2.1%
35 82
3.7%
34 166
7.5%
33 46
 
2.1%
32 32
 
1.4%
31 8
 
0.4%
30 8
 
0.4%
29 2
 
0.1%
28 13
 
0.6%

velmedia
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.0%
Missing14
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.3043241
Minimum0.30000001
Maximum13.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:04.970274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.1
Q11.7
median2.5
Q34.4000001
95-th percentile7.5
Maximum13.1
Range12.8
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.0683041
Coefficient of variation (CV)0.62593862
Kurtosis0.89055544
Mean3.3043241
Median Absolute Deviation (MAD)1.1
Skewness1.0898153
Sum7259.6
Variance4.2778821
MonotonicityNot monotonic
2025-02-25T23:05:05.086726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1.899999976 197
 
8.9%
1.700000048 186
 
8.4%
2.200000048 181
 
8.2%
1.399999976 173
 
7.8%
1.100000024 134
 
6.1%
2.5 131
 
5.9%
2.799999952 112
 
5.1%
3.099999905 92
 
4.2%
3.900000095 84
 
3.8%
3.599999905 72
 
3.3%
Other values (33) 835
37.8%
ValueCountFrequency (%)
0.3000000119 12
 
0.5%
0.6000000238 24
 
1.1%
0.8000000119 64
 
2.9%
1.100000024 134
6.1%
1.399999976 173
7.8%
1.700000048 186
8.4%
1.899999976 197
8.9%
2.200000048 181
8.2%
2.5 131
5.9%
2.799999952 112
5.1%
ValueCountFrequency (%)
13.10000038 1
 
< 0.1%
12.80000019 1
 
< 0.1%
11.89999962 2
 
0.1%
11.39999962 2
 
0.1%
10.80000019 1
 
< 0.1%
10.60000038 2
 
0.1%
10.30000019 1
 
< 0.1%
10 6
0.3%
9.699999809 2
 
0.1%
9.399999619 4
0.2%

racha
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)3.3%
Missing18
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean8.9808938
Minimum2.5
Maximum26.700001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2025-02-25T23:05:05.194657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.1999998
Q15.5999999
median8.3000002
Q311.7
95-th percentile16.4
Maximum26.700001
Range24.200001
Interquartile range (IQR)6.0999999

Descriptive statistics

Standard deviation4.001039
Coefficient of variation (CV)0.44550566
Kurtosis-0.038142439
Mean8.9808938
Median Absolute Deviation (MAD)3
Skewness0.72178006
Sum19695.1
Variance16.008314
MonotonicityNot monotonic
2025-02-25T23:05:05.317529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 111
 
5.0%
5.300000191 103
 
4.7%
4.699999809 89
 
4.0%
5.800000191 80
 
3.6%
4.199999809 77
 
3.5%
6.099999905 66
 
3.0%
5.599999905 64
 
2.9%
9.699999809 60
 
2.7%
9.199999809 57
 
2.6%
11.10000038 56
 
2.5%
Other values (63) 1430
64.7%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
2.799999952 1
 
< 0.1%
3.099999905 7
 
0.3%
3.299999952 14
 
0.6%
3.599999905 28
 
1.3%
3.900000095 55
2.5%
4.199999809 77
3.5%
4.400000095 52
2.4%
4.699999809 89
4.0%
5 111
5.0%
ValueCountFrequency (%)
26.70000076 1
 
< 0.1%
23.89999962 1
 
< 0.1%
23.10000038 1
 
< 0.1%
22.5 2
0.1%
22.20000076 4
0.2%
21.70000076 1
 
< 0.1%
21.39999962 1
 
< 0.1%
21.10000038 1
 
< 0.1%
20.79999924 2
0.1%
20.29999924 1
 
< 0.1%
Distinct145
Distinct (%)6.6%
Missing18
Missing (%)0.8%
Memory size17.4 KiB
2025-02-25T23:05:05.517988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0547196
Min length5

Characters and Unicode

Total characters11085
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04:30
2nd row19:20
3rd row10:50
4th rowVarias
5th rowVarias
ValueCountFrequency (%)
varias 120
 
5.5%
00:10 53
 
2.4%
13:40 36
 
1.6%
00:20 31
 
1.4%
11:20 30
 
1.4%
15:00 29
 
1.3%
14:30 28
 
1.3%
10:50 28
 
1.3%
12:30 27
 
1.2%
13:00 27
 
1.2%
Other values (135) 1784
81.3%
2025-02-25T23:05:05.798055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3428
30.9%
: 2073
18.7%
1 1695
15.3%
2 866
 
7.8%
3 671
 
6.1%
4 559
 
5.0%
5 559
 
5.0%
a 240
 
2.2%
6 147
 
1.3%
7 137
 
1.2%
Other values (6) 710
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3428
30.9%
: 2073
18.7%
1 1695
15.3%
2 866
 
7.8%
3 671
 
6.1%
4 559
 
5.0%
5 559
 
5.0%
a 240
 
2.2%
6 147
 
1.3%
7 137
 
1.2%
Other values (6) 710
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3428
30.9%
: 2073
18.7%
1 1695
15.3%
2 866
 
7.8%
3 671
 
6.1%
4 559
 
5.0%
5 559
 
5.0%
a 240
 
2.2%
6 147
 
1.3%
7 137
 
1.2%
Other values (6) 710
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3428
30.9%
: 2073
18.7%
1 1695
15.3%
2 866
 
7.8%
3 671
 
6.1%
4 559
 
5.0%
5 559
 
5.0%
a 240
 
2.2%
6 147
 
1.3%
7 137
 
1.2%
Other values (6) 710
 
6.4%

hrMedia
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)3.7%
Missing22
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean69.546825
Minimum9
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2025-02-25T23:05:05.903485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile43
Q164
median72
Q379
95-th percentile86
Maximum97
Range88
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.92717
Coefficient of variation (CV)0.18587721
Kurtosis1.5545413
Mean69.546825
Median Absolute Deviation (MAD)7
Skewness-1.0932961
Sum152238
Variance167.11172
MonotonicityNot monotonic
2025-02-25T23:05:06.010916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 91
 
4.1%
77 87
 
3.9%
73 87
 
3.9%
71 83
 
3.8%
76 82
 
3.7%
75 81
 
3.7%
80 81
 
3.7%
79 77
 
3.5%
81 77
 
3.5%
69 76
 
3.4%
Other values (71) 1367
61.8%
ValueCountFrequency (%)
9 1
< 0.1%
10 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
25 2
0.1%
26 1
< 0.1%
ValueCountFrequency (%)
97 2
 
0.1%
96 1
 
< 0.1%
95 3
 
0.1%
94 3
 
0.1%
93 7
0.3%
92 6
 
0.3%
91 8
0.4%
90 11
0.5%
89 11
0.5%
88 17
0.8%

hrMax
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)2.8%
Missing20
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean84.306253
Minimum23
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2025-02-25T23:05:06.119408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile66
Q181
median86
Q390
95-th percentile95
Maximum100
Range77
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.4194859
Coefficient of variation (CV)0.11172939
Kurtosis5.3266921
Mean84.306253
Median Absolute Deviation (MAD)5
Skewness-1.8618319
Sum184715
Variance88.726714
MonotonicityNot monotonic
2025-02-25T23:05:06.233184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 167
 
7.6%
85 138
 
6.2%
87 136
 
6.2%
89 129
 
5.8%
88 119
 
5.4%
91 118
 
5.3%
86 114
 
5.2%
92 105
 
4.7%
84 97
 
4.4%
83 91
 
4.1%
Other values (52) 977
44.2%
ValueCountFrequency (%)
23 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
0.1%
38 1
 
< 0.1%
39 7
0.3%
40 1
 
< 0.1%
42 2
 
0.1%
43 2
 
0.1%
46 3
0.1%
47 2
 
0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 7
 
0.3%
98 10
 
0.5%
97 25
 
1.1%
96 41
 
1.9%
95 62
2.8%
94 67
3.0%
93 88
4.0%
92 105
4.7%
91 118
5.3%
Distinct146
Distinct (%)6.7%
Missing20
Missing (%)0.9%
Memory size17.4 KiB
2025-02-25T23:05:06.417353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2373346
Min length5

Characters and Unicode

Total characters11475
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row18:20
2nd rowVarias
3rd row23:30
4th row00:00
5th row19:30
ValueCountFrequency (%)
varias 520
 
23.7%
00:00 132
 
6.0%
23:59 37
 
1.7%
23:40 34
 
1.6%
00:10 32
 
1.5%
23:50 30
 
1.4%
23:30 27
 
1.2%
18:00 27
 
1.2%
00:30 25
 
1.1%
19:40 23
 
1.0%
Other values (136) 1304
59.5%
2025-02-25T23:05:06.824578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3091
26.9%
: 1671
14.6%
a 1040
 
9.1%
1 910
 
7.9%
2 866
 
7.5%
3 554
 
4.8%
i 520
 
4.5%
r 520
 
4.5%
s 520
 
4.5%
V 520
 
4.5%
Other values (6) 1263
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3091
26.9%
: 1671
14.6%
a 1040
 
9.1%
1 910
 
7.9%
2 866
 
7.5%
3 554
 
4.8%
i 520
 
4.5%
r 520
 
4.5%
s 520
 
4.5%
V 520
 
4.5%
Other values (6) 1263
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3091
26.9%
: 1671
14.6%
a 1040
 
9.1%
1 910
 
7.9%
2 866
 
7.5%
3 554
 
4.8%
i 520
 
4.5%
r 520
 
4.5%
s 520
 
4.5%
V 520
 
4.5%
Other values (6) 1263
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3091
26.9%
: 1671
14.6%
a 1040
 
9.1%
1 910
 
7.9%
2 866
 
7.5%
3 554
 
4.8%
i 520
 
4.5%
r 520
 
4.5%
s 520
 
4.5%
V 520
 
4.5%
Other values (6) 1263
11.0%

hrMin
Real number (ℝ)

High correlation 

Distinct89
Distinct (%)4.1%
Missing20
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean52.025103
Minimum2
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2025-02-25T23:05:06.930171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q141
median55
Q366
95-th percentile76
Maximum93
Range91
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.514321
Coefficient of variation (CV)0.33665134
Kurtosis-0.37978829
Mean52.025103
Median Absolute Deviation (MAD)12
Skewness-0.53756332
Sum113987
Variance306.75142
MonotonicityNot monotonic
2025-02-25T23:05:07.037689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 61
 
2.8%
59 58
 
2.6%
62 56
 
2.5%
60 56
 
2.5%
57 54
 
2.4%
61 54
 
2.4%
67 54
 
2.4%
70 52
 
2.4%
63 52
 
2.4%
72 50
 
2.3%
Other values (79) 1644
74.4%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 5
0.2%
4 3
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 4
0.2%
8 7
0.3%
10 6
0.3%
11 4
0.2%
12 5
0.2%
ValueCountFrequency (%)
93 1
 
< 0.1%
92 1
 
< 0.1%
90 1
 
< 0.1%
88 3
0.1%
87 1
 
< 0.1%
86 2
0.1%
85 3
0.1%
84 3
0.1%
83 2
0.1%
82 4
0.2%
Distinct145
Distinct (%)6.6%
Missing20
Missing (%)0.9%
Memory size17.4 KiB
2025-02-25T23:05:07.237737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0565952
Min length5

Characters and Unicode

Total characters11079
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row10:00
2nd row21:00
3rd row03:00
4th rowVarias
5th row09:50
ValueCountFrequency (%)
varias 124
 
5.7%
08:40 42
 
1.9%
07:20 42
 
1.9%
07:40 42
 
1.9%
08:50 41
 
1.9%
07:10 40
 
1.8%
08:30 40
 
1.8%
09:00 39
 
1.8%
07:50 37
 
1.7%
09:10 37
 
1.7%
Other values (135) 1707
77.9%
2025-02-25T23:05:07.508087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3630
32.8%
: 2067
18.7%
1 1401
 
12.6%
2 692
 
6.2%
3 565
 
5.1%
5 517
 
4.7%
4 446
 
4.0%
7 298
 
2.7%
8 283
 
2.6%
9 267
 
2.4%
Other values (6) 913
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3630
32.8%
: 2067
18.7%
1 1401
 
12.6%
2 692
 
6.2%
3 565
 
5.1%
5 517
 
4.7%
4 446
 
4.0%
7 298
 
2.7%
8 283
 
2.6%
9 267
 
2.4%
Other values (6) 913
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3630
32.8%
: 2067
18.7%
1 1401
 
12.6%
2 692
 
6.2%
3 565
 
5.1%
5 517
 
4.7%
4 446
 
4.0%
7 298
 
2.7%
8 283
 
2.6%
9 267
 
2.4%
Other values (6) 913
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3630
32.8%
: 2067
18.7%
1 1401
 
12.6%
2 692
 
6.2%
3 565
 
5.1%
5 517
 
4.7%
4 446
 
4.0%
7 298
 
2.7%
8 283
 
2.6%
9 267
 
2.4%
Other values (6) 913
 
8.2%

Interactions

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2025-02-25T23:05:00.424669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-25T23:05:07.596628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
dirhrMaxhrMediahrMinprecrachatmaxtmedtminvelmedia
dir1.000-0.055-0.148-0.186-0.050-0.177-0.065-0.132-0.195-0.162
hrMax-0.0551.0000.7030.5550.303-0.0600.1120.1710.219-0.126
hrMedia-0.1480.7031.0000.8630.246-0.142-0.0980.0080.116-0.071
hrMin-0.1860.5550.8631.0000.185-0.127-0.130-0.0110.119-0.048
prec-0.0500.3030.2460.1851.0000.269-0.272-0.224-0.1640.173
racha-0.177-0.060-0.142-0.1270.2691.000-0.196-0.161-0.1250.803
tmax-0.0650.112-0.098-0.130-0.272-0.1961.0000.9710.887-0.287
tmed-0.1320.1710.008-0.011-0.224-0.1610.9711.0000.969-0.226
tmin-0.1950.2190.1160.119-0.164-0.1250.8870.9691.000-0.158
velmedia-0.162-0.126-0.071-0.0480.1730.803-0.287-0.226-0.1581.000

Missing values

2025-02-25T23:05:01.387393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-25T23:05:01.531769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-25T23:05:01.760907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin
02019-01-016076XMARBELLA, PUERTOMALAGA2.013.90.09.606:3118.20000110:1433.01.75.004:3063.079.018:2037.010:00
12019-01-026076XMARBELLA, PUERTOMALAGA2.014.60.09.407:3219.90000016:0435.01.16.119:2056.069.0Varias36.021:00
22019-01-036076XMARBELLA, PUERTOMALAGA2.013.10.09.606:2216.60000013:226.04.710.610:5070.081.023:3043.003:00
32019-01-046076XMARBELLA, PUERTOMALAGA2.012.70.09.323:3516.100000Varias99.05.38.9Varias69.078.000:0060.0Varias
42019-01-056076XMARBELLA, PUERTOMALAGA2.012.40.07.907:0317.00000010:1135.01.95.8Varias67.082.019:3046.009:50
52019-01-066076XMARBELLA, PUERTOMALAGA2.012.80.07.506:4118.20000115:5433.01.75.004:2063.071.018:2047.014:30
62019-01-076076XMARBELLA, PUERTOMALAGA2.013.80.08.606:3319.10000010:2036.02.25.619:1065.077.018:2042.008:10
72019-01-086076XMARBELLA, PUERTOMALAGA2.012.60.09.002:2516.10000012:027.02.810.010:4070.083.0Varias58.004:00
82019-01-096076XMARBELLA, PUERTOMALAGA2.013.80.07.507:2320.00000014:3622.03.37.212:3066.083.0Varias52.020:40
92019-01-106076XMARBELLA, PUERTOMALAGA2.014.00.010.701:0617.20000109:1111.01.98.911:3064.080.019:5036.009:10
fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin
22012024-12-306076XMARBELLA, PUERTOMALAGA2.015.211.413.022:0617.29999911:1499.04.210.800:1056.070.002:1047.0Varias
22022024-12-316076XMARBELLA, PUERTOMALAGA2.014.70.011.202:0518.20000114:475.03.111.101:0062.090.0Varias44.013:30
22032025-01-016076XMARBELLA, PUERTOMALAGA2.013.20.610.122:4316.40000013:2512.02.28.910:1066.077.010:2058.013:30
22042025-01-016076XMARBELLA, PUERTOMALAGA2.013.20.610.122:4316.40000013:2512.02.28.910:1066.077.010:2058.013:30
22052025-01-026076XMARBELLA, PUERTOMALAGA2.013.80.010.200:0317.50000012:1199.02.55.8Varias60.074.0Varias44.012:20
22062025-01-036076XMARBELLA, PUERTOMALAGA2.013.90.09.505:5418.29999910:2134.03.16.101:5072.084.018:5038.023:40
22072025-01-046076XMARBELLA, PUERTOMALAGA2.015.40.010.203:5520.70000114:5622.02.510.313:3041.062.023:4020.009:30
22082025-01-056076XMARBELLA, PUERTOMALAGA2.014.231.611.007:4817.40000010:3224.04.417.518:2064.086.0Varias51.017:50
22092025-01-066076XMARBELLA, PUERTOMALAGA2.015.50.612.023:5919.00000013:5526.02.512.215:5064.095.0Varias24.014:00
22102025-01-076076XMARBELLA, PUERTOMALAGA2.013.20.09.007:0917.50000011:3135.03.35.806:3046.058.000:1025.011:20

Duplicate rows

Most frequently occurring

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin# duplicates
02019-07-016076XMARBELLA, PUERTOMALAGA2.023.7999990.020.105:4027.40000016:1122.02.510.013:4070.090.001:3063.016:102
12020-01-016076XMARBELLA, PUERTOMALAGA2.013.6000000.010.3Varias17.00000011:1634.01.75.3Varias74.086.0Varias63.011:102
22020-07-016076XMARBELLA, PUERTOMALAGA2.023.1000000.019.005:3327.20000116:0734.01.73.906:1072.090.000:0061.017:002
32021-01-016076XMARBELLA, PUERTOMALAGA2.013.7000000.010.1Varias17.29999913:5226.03.912.211:0044.081.000:4020.015:002
42021-07-016076XMARBELLA, PUERTOMALAGA2.022.0000000.019.602:4324.50000011:1522.01.13.921:4080.092.0Varias74.011:202
52022-01-016076XMARBELLA, PUERTOMALAGA2.013.2000000.010.408:0615.90000016:2210.01.75.012:2086.095.002:3076.015:502
62022-07-016076XMARBELLA, PUERTOMALAGA2.020.6000000.018.005:1923.10000008:2810.05.814.210:4083.089.015:1073.009:502
72023-01-016076XMARBELLA, PUERTOMALAGA2.016.4000000.011.903:4820.90000010:5934.01.15.605:5036.057.018:5015.011:002
82023-07-016076XMARBELLA, PUERTOMALAGA2.024.600000NaN21.901:2327.29999915:5613.01.15.009:0067.086.0Varias52.007:102
92024-01-016076XMARBELLA, PUERTOMALAGA2.017.200001NaN14.101:0320.29999914:5032.01.95.822:0046.065.019:2024.010:002